


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a			:			Union[str, Any]												=					logging.get_logger(__name__)

a			:			Union[str, Any]												=					{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					'deta'
					__UpperCAmelCase							  =					{
					    'hidden_size': 'd_model',
					    'num_attention_heads': 'encoder_attention_heads',
					}






					def __init__(					self			,							snake_case_=None			,							snake_case_=9_0_0			,							snake_case_=2_0_4_8			,							snake_case_=6			,							snake_case_=2_0_4_8			,							snake_case_=8			,							snake_case_=6			,							snake_case_=1_0_2_4			,							snake_case_=8			,							snake_case_=0.0			,							snake_case_=True			,							snake_case_="relu"			,							snake_case_=2_5_6			,							snake_case_=0.1			,							snake_case_=0.0			,							snake_case_=0.0			,							snake_case_=0.0_2			,							snake_case_=1.0			,							snake_case_=True			,							snake_case_=False			,							snake_case_="sine"			,							snake_case_=5			,							snake_case_=4			,							snake_case_=4			,							snake_case_=True			,							snake_case_=3_0_0			,							snake_case_=True			,							snake_case_=True			,							snake_case_=1			,							snake_case_=5			,							snake_case_=2			,							snake_case_=1			,							snake_case_=1			,							snake_case_=5			,							snake_case_=2			,							snake_case_=0.1			,							snake_case_=0.2_5			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''    )
											__lowercase			       = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']    )
								else:
											if isinstance(snake_case_			,							snake_case_    ):
														__lowercase			       = backbone_config.pop('''model_type'''    )
														__lowercase			       = CONFIG_MAPPING[backbone_model_type]
														__lowercase			       = config_class.from_dict(snake_case_    )

								__lowercase			       = backbone_config
								__lowercase			       = num_queries
								__lowercase			       = max_position_embeddings
								__lowercase			       = d_model
								__lowercase			       = encoder_ffn_dim
								__lowercase			       = encoder_layers
								__lowercase			       = encoder_attention_heads
								__lowercase			       = decoder_ffn_dim
								__lowercase			       = decoder_layers
								__lowercase			       = decoder_attention_heads
								__lowercase			       = dropout
								__lowercase			       = attention_dropout
								__lowercase			       = activation_dropout
								__lowercase			       = activation_function
								__lowercase			       = init_std
								__lowercase			       = init_xavier_std
								__lowercase			       = encoder_layerdrop
								__lowercase			       = auxiliary_loss
								__lowercase			       = position_embedding_type
								# deformable attributes
								__lowercase			       = num_feature_levels
								__lowercase			       = encoder_n_points
								__lowercase			       = decoder_n_points
								__lowercase			       = two_stage
								__lowercase			       = two_stage_num_proposals
								__lowercase			       = with_box_refine
								__lowercase			       = assign_first_stage
								if two_stage is True and with_box_refine is False:
											raise ValueError('''If two_stage is True, with_box_refine must be True.'''    )
								# Hungarian matcher
								__lowercase			       = class_cost
								__lowercase			       = bbox_cost
								__lowercase			       = giou_cost
								# Loss coefficients
								__lowercase			       = mask_loss_coefficient
								__lowercase			       = dice_loss_coefficient
								__lowercase			       = bbox_loss_coefficient
								__lowercase			       = giou_loss_coefficient
								__lowercase			       = eos_coefficient
								__lowercase			       = focal_alpha
								super().__init__(is_encoder_decoder=snake_case_			,							**snake_case_    )






					@property
					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								return self.encoder_attention_heads






					@property
					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								return self.d_model






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = copy.deepcopy(self.__dict__    )
								__lowercase			       = self.backbone_config.to_dict()
								__lowercase			       = self.__class__.model_type
								return output






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None   ,    snake_case = False   ,    snake_case = False   ,    snake_case = None   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(
						    features=snake_case   ,    cache_dir=snake_case   ,    keep_in_memory=snake_case   ,    streaming=snake_case   ,    num_proc=snake_case   ,    **snake_case   ,    )
						UpperCamelCase__       =							Generator(
						    cache_dir=snake_case   ,    features=snake_case   ,    generator=snake_case   ,    gen_kwargs=snake_case   ,    **snake_case   ,    )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.streaming:
									UpperCamelCase__       =							self.builder.as_streaming_dataset(split="train"  )
						# Build regular (map-style) dataset
						else:
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None

									self.builder.download_and_prepare(
									    download_config=snake_case   ,    download_mode=snake_case   ,    verification_mode=snake_case   ,    base_path=snake_case   ,    num_proc=self.num_proc   ,    )
									UpperCamelCase__       =							self.builder.as_dataset(
									    split="train"   ,    verification_mode=snake_case   ,    in_memory=self.keep_in_memory  )
						return dataset


